# -*- coding: UTF-8 -*-
# !/usr/bin/python
# @time     :2019/5/12 13:16
# @author   :Mo
# @function :chatbot based search, encode sentence_vec by bert

def chatbot_sentence_vec_by_bert_own():
    """bert encode is writted by my own"""
    from FeatureProject.bert.extract_keras_bert_feature import KerasBertVector
    from conf.path_config import chicken_and_gossip_path
    from utils.text_tools import txtRead
    import numpy as np

    # 读取数据和一些参数，这里只取了100个标准问题
    topk = 5
    matrix_ques_save_path = "doc_vecs_chicken_and_gossip"
    questions = txtRead(chicken_and_gossip_path, encodeType='utf-8')
    ques = [ques.split('\t')[0] for ques in questions][0:100]

    # 生成标准问题的bert句向量
    bert_vector = KerasBertVector()
    ques_basic_vecs = bert_vector.bert_encode(ques)

    # 线上你可以生成，直接调用，然后直接load就好
    np.savetxt(matrix_ques_save_path, ques_basic_vecs)
    # matrix_ques = np.loadtxt(matrix_ques_save_path)

    query_bert_vec = bert_vector.bert_encode(["小姜机器人是什么"])[0]
    query_bert_vec = np.array(query_bert_vec)
    print(query_bert_vec)
    # 矩阵点乘，很快的，你也可以用annoy等工具，计算就更加快了
    qq_score = np.sum(query_bert_vec * ques_basic_vecs, axis=1) / np.linalg.norm(ques_basic_vecs, axis=1)
    topk_idx = np.argsort(qq_score)[::-1][:topk]
    for idx in topk_idx:
        print('小姜机器人回答检索： %s\t%s' % (qq_score[idx], questions[idx]))


    while True:
        print("你的问题:")
        query = input()
        query_bert_vec = bert_vector.bert_encode([query])[0]
        # 矩阵点乘，很快的，你也可以用annoy等工具，计算就更加快了
        qq_score = np.sum(query_bert_vec * ques_basic_vecs, axis=1) / np.linalg.norm(ques_basic_vecs, axis=1)
        topk_idx = np.argsort(qq_score)[::-1][:topk]
        for idx in topk_idx:
            print('小姜机器人回答检索： %s\t%s' % (qq_score[idx], questions[idx]))


def chatbot_sentence_vec_by_bert_bertasserver():
    """bert encode is used bert as server"""
    from conf.path_config import chicken_and_gossip_path
    from bert_serving.client import BertClient
    from utils.text_tools import txtRead
    import numpy as np

    topk = 5
    matrix_ques_save_path = "doc_vecs_chicken_and_gossip"
    questions = txtRead(chicken_and_gossip_path, encodeType='utf-8')
    ques = [ques.split('\t')[0] for ques in questions][0:100]

    bc = BertClient(ip = 'localhost')
    doc_vecs = bc.encode(ques)
    np.savetxt(matrix_ques_save_path, doc_vecs)
    # matrix_ques = np.loadtxt(matrix_ques_save_path)

    while True:
        query = input('你问: ')
        query_vec = bc.encode([query])[0]
        # compute normalized dot product as score
        score = np.sum(query_vec * doc_vecs, axis=1) / np.linalg.norm(doc_vecs, axis=1)
        topk_idx = np.argsort(score)[::-1][:topk]
        for idx in topk_idx:
            print('小姜机器人回答： %s\t%s' % (score[idx], questions[idx]))


if __name__=="__main__":
    chatbot_sentence_vec_by_bert_own()
    # chatbot_sentence_vec_by_bert_bertasserver()


# result
# 小姜机器人是什么
# Tokens: ['[CLS]', '小', '姜', '机', '器', '人', '是', '什', '么', '[SEP]']
# (1, 32, 768)
# [CLS] [768] [1.0393640995025635, -0.31394684314727783, -0.08567211031913757, -0.12281288206577301, 0.5685704946517944, -0.6734460592269897, 0.3936116099357605, -0.14657911658287048, -0.5451663732528687, 0.8552711009979248, -0.3694000840187073, -0.0018898732960224152, -0.005383625626564026, 0.15833452343940735, 2.392857074737549, -1.370668649673462, 0.9063063263893127, -1.142601490020752, -0.3274427652359009, -0.0483686625957489, -0.3603387475013733, 0.3079147934913635, -1.9008374214172363, -0.3452332615852356, 0.2769089639186859, 0.36075833439826965, -0.6220439672470093, -0.37962478399276733, -0.22173452377319336, 0.23706544935703278, -0.35481616854667664, 0.08377914130687714, 0.07834005355834961, 0.01608183979988098, 0.5389248728752136, -0.05104628950357437, -0.0463351309299469, 0.6286687850952148, 0.5550581812858582, 0.29511526226997375, -0.18117117881774902, -0.8892947435379028, 0.35202541947364807, 2.5349209308624268, 0.2730821371078491, -0.5735995769500732, 0.5421597957611084, 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#  -7.10590124e-01  1.75247788e-01 -1.01621938e+00 -1.97003931e-01]
# 小姜机器人回答检索： 16.359036422243992	人太多了!找不到你给我回的了	=。=
#
# 小姜机器人回答检索： 16.359036422243992	就欺负你要怎么!就喜欢欺负你	==
#
# 小姜机器人回答检索： 16.359036422243992	你大爷不认识我!要不先带你去	=。=
#
# 小姜机器人回答检索： 16.35903642224399	什么让你这么伤心	如果我的无心之谈害你流泪，那我和你道歉，我爱你。
#
# 小姜机器人回答检索： 16.35903642224399	你敢不敢	你敢我就敢，小样，敢跟我叫板
#
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